[1]} use the "encode-process-decode" [2]}, [3]} paradigm along with dilated convolutional networks to capture turbulent dynamics seen in high-resolution solutions only by training on low spatial and temporal resolutions. Their approach beats existing neural PDE solvers in addition to the state-of-the-art Athena++ engine [4]}. We take inspiration of this approach but replace the process module by an interpolation module, to allow the model to capture spatial correlations between known points and new query points.
